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Clinical Trials/NCT07265011
NCT07265011CompletedNot Applicable

Prospective Validation of an AI-Driven Ultrasound-Based Method for Estimating Hepatic Steatosis Using MRI-Derived Fat Fraction as Reference in Pediatric Metabolic Dysfunction-Associated Steatotic Liver Disease

Jae Won Choi1 site in 1 country50 target enrollmentStarted: December 31, 2024Last updated:
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Trial Snapshot

Phase
Not Applicable
Status
Completed
Sponsor
Enrollment
50
Locations
1
Primary Endpoint
Agreement Between AI-Predicted Ultrasound Fat Fraction (AI-USFF) and MRI Proton Density Fat Fraction (MRI-PDFF)
Study OverviewStudy DesignEligibility CriteriaOutcomesInvestigatorsSitesRecords & IdentifiersSimilar Trials

Study Overview

Brief Summary

The purpose of this study is to validate an artificial intelligence (AI)-based algorithm that estimates hepatic steatosis using ultrasound (US) B-mode images in pediatric participants with metabolic dysfunction-associated steatotic liver disease (MASLD). The MRI proton density fat fraction (MRI-PDFF) serves as the reference standard for hepatic fat quantification.

Study Design

Study Type
Interventional
Allocation
Na
Intervention Model
Single Group
Primary Purpose
Diagnostic
Masking
None

Eligibility Criteria

Ages
8 Years to 18 Years (Child, Adult)
Sex
All
Accepts Healthy Volunteers
Yes

Inclusion Criteria

  • •participants aged 8 to 18 years clinically indicated for liver ultrasound examination to evaluate hepatic steatosis
  • •participants with suspected or known metabolic dysfunction-associated steatotic liver disease (MASLD)
  • •able to understand the study purpose and provide written informed consent (from both participant and legal guardian).
  • •agree to undergo same-day liver MRI examination in addition to the ultrasound

Exclusion Criteria

  • •unable to cooperate with imaging procedures
  • •parent or legal guardian unable to understand the study explanation
  • •contraindications to MRI
  • •determined by the investigator to be otherwise unsuitable for participation after consultation

Outcomes

Primary Outcomes

Agreement Between AI-Predicted Ultrasound Fat Fraction (AI-USFF) and MRI Proton Density Fat Fraction (MRI-PDFF)

Time Frame: At time of imaging (single visit)

Reference standard: MRI-PDFF (percentage) \- intraclass correlation coefficient (ICC)

Secondary Outcomes

  • Diagnostic Performance of AI-USFF for MRI-Based Hepatic Steatosis Grades(At time of imaging (single visit))
  • Inter-Vendor Reproducibility of AI-USFF(At time of imaging (single visit))
  • Correlation Between AI-USFF and MRI-PDFF(At time of imaging (single visit))

Investigators

Sponsor
Jae Won Choi
Sponsor Class
Other
Responsible Party
Sponsor Investigator
Principal Investigator

Jae Won Choi

Clinical Assistant Professor

Seoul National University Hospital

Study Sites (1)

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